A task decomposition approach to using neural networks for the interpretation of bioprocess data

1992 ◽  
Vol 17 ◽  
pp. 447-452
Author(s):  
G.K. Raju ◽  
C.L. Cooney
Keyword(s):  
Neural Networks ◽  
Decomposition Approach ◽  
Task Decomposition

scholarly journals Dynamical Analysis of DTNN with Impulsive Effect

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Chao Chen ◽  
Zhenkun Huang ◽  
Honghua Bin ◽  
Xiaohui Liu
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Dynamical Analysis ◽  
Impulsive Effect ◽  
Decomposition Approach ◽  
Delayed Neural Networks ◽  
Delay Difference ◽  
New Criteria

We present dynamical analysis of discrete-time delayed neural networks with impulsive effect. Under impulsive effect, we derive some new criteria for the invariance and attractivity of discrete-time neural networks by using decomposition approach and delay difference inequalities. Our results improve or extend the existing ones.

Delay-Dependent H∞ Filtering for Neural Networks with Time Delay

2014 ◽  
Vol 511-512 ◽  
pp. 875-879 ◽  
Author(s):  
Ya Jun Li ◽  
Yan Nong Liang
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Filter Design ◽  
Linear Matrix ◽  
Globally Asymptotically Stable ◽  
H Infinity ◽  
Decomposition Approach ◽  
Delay Decomposition Approach ◽  
Error System ◽  
Delay Dependent

The H{infinity} filter design problem of recurrent neural networks with time delay is considered. Based on delay decomposition approach, the delay-dependent condition is derived to ensure that the filtering error system is globally asymptotically stable with a guaranteed performance. And the design of such a filter can be solved by the linear matrix inequality. A numerical example is provided to demonstrate that the developed approach is efficient.

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